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Found 13 papers, 8 papers with code
CCMR: High Resolution Optical Flow Estimation via Coarse-to-Fine Context-Guided Motion Reasoning
Attention-based motion aggregation concepts have recently shown their usefulness in optical flow estimation, in particular when it comes to handling occluded regions.
Detection Defenses: An Empty Promise against Adversarial Patch Attacks on Optical Flow
In this paper, we thoroughly examine the currently available detect-and-remove defenses ILP and LGS for a wide selection of state-of-the-art optical flow methods, and illuminate their side effects on the quality and robustness of the final flow predictions.
Distracting Downpour: Adversarial Weather Attacks for Motion Estimation
Current adversarial attacks on motion estimation, or optical flow, optimize small per-pixel perturbations, which are unlikely to appear in the real world.
Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo
While recent methods for motion and stereo estimation recover an unprecedented amount of details, such highly detailed structures are neither adequately reflected in the data of existing benchmarks nor their evaluation methodology.
High Resolution Multi-Scale RAFT (Robust Vision Challenge 2022)
In this report, we present our optical flow approach, MS-RAFT+, that won the Robust Vision Challenge 2022.
Ranked #3 on
Optical Flow Estimation
on Spring
Attacking Motion Estimation with Adversarial Snow
Current adversarial attacks for motion estimation (optical flow) optimize small per-pixel perturbations, which are unlikely to appear in the real world.
Multi-Scale RAFT: Combining Hierarchical Concepts for Learning-based Optical FLow Estimation
Many classical and learning-based optical flow methods rely on hierarchical concepts to improve both accuracy and robustness.
M-FUSE: Multi-frame Fusion for Scene Flow Estimation
Secondly, and even more importantly, exploiting the specific modeling concepts of RAFT-3D, we propose a U-Net architecture that performs a fusion of forward and backward flow estimates and hence allows to integrate temporal information on demand.
Ranked #1 on
Scene Flow Estimation
on Spring
Blind Image Inpainting with Sparse Directional Filter Dictionaries for Lightweight CNNs
Blind inpainting algorithms based on deep learning architectures have shown a remarkable performance in recent years, typically outperforming model-based methods both in terms of image quality and run time.
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow
Recent optical flow methods are almost exclusively judged in terms of accuracy, while their robustness is often neglected.
Subjective Annotation for a Frame Interpolation Benchmark using Artefact Amplification
This re-ranking not only shows the necessity of visual quality assessment as another evaluation metric for optical flow and frame interpolation benchmarks, the results also provide the ground truth for designing novel image quality assessment (IQA) methods dedicated to perceptual quality of interpolated images.
ProFlow: Learning to Predict Optical Flow
By relating forward and backward motion these learned models not only allow to infer valuable motion information based on the backward flow, they also help to improve the performance at occlusions, where a reliable prediction is particularly useful.
Ranked #15 on
Optical Flow Estimation
on Sintel-clean
Direct Variational Perspective Shape from Shading with Cartesian Depth Parametrisation
First, we propose a novel variational model that operates directly on the Cartesian depth.
